The role of energy technology innovation in reducing greenhouse gas emissions: A case study of Canada
Bibliographic record
Abstract
Understanding the influence of energy technology innovation in reducing a country's greenhouse gas emissions requires a systematic review to characterize the existing system. A comprehensive data review of available financing mechanisms and investments by government and industry is undertaken for the case of Canada, coupled with an organized examination of existing international, federal, and regional climate policies that advance innovation. Results indicate that investments from early research and development through to capital expenditures are heavily weighted towards fossil fuels. Though federal efforts to meet international commitments have been unsuccessful, regions implementing high carbon fuel phase-out, renewable portfolio standards, and feed-in-tariffs were found to be successful in reducing emissions. Financing for clean energy projects is readily available; however, there is no complete database available for investors to discover these opportunities. To enhance clean energy innovation in Canada and enable success in emissions reductions, we suggest that investments (from research and development to capital expenditures) and regional policies should be aligned with federal commitments, along with clear communication of available financing to attract clean energy investors. Our approach to a systematic review is broadly applicable to other regions where there is interest in understanding and improving the role of innovation in reducing greenhouse gas emissions, particularly in countries with federalist political systems and large fossil fuel reserves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".